The reliable operation of rolling element bearings is crucial in various industrial applications, yet predicting their failure remains challenging. This paper presents a comprehensive approach for determining failure thresholds in rolling element bearings. By integrating experimental testing, statistical analysis, and predictive modeling, this approach aims to identify key indicators and parameters that precede bearing failure. Through accelerated life testing and condition monitoring, data on bearing performance, vibration, temperature, and lubricant condition are collected and analyzed. Statistical methods such as Weibull analysis and survival analysis are applied to quantify the probability of failure and estimate the remaining useful life of bearings. Additionally, machine learning techniques are employed to develop predictive models that correlate operational parameters with bearing degradation and failure. By combining these approaches, a holistic understanding of bearing failure mechanisms is achieved, enabling proactive maintenance strategies and improved reliability in industrial systems.